Human-Induced Runoff Change in Northeast ChinaAijing Zhang1; Chi Zhang2; Jinggang Chu3; and Guobin Fu4

Abstract: Human activities are known to increase interference with runoff. Conversely, human activities related to utilizing and man-aging water resources are primarily determined by annual runoff processes dominated by precipitation distribution. With this view, thesoil and water assessment tool was used to quantify human-induced annual runoff changes at different periods and under differentpatterns of precipitation in seven catchments in Northeast China. The conclusions are as follows. First, although human activities havedistinct regional characteristics, an increase in reduced runoff is found for the catchments under investigation; human-induced runoffchanges are more significant in the catchment where water resources are limited. Second, the interannual runoff distribution is signifi-cantly disturbed in the catchment with large reservoirs. Third, human-induced runoff changes are similar under all patterns of precipi-tation in the catchment where annual precipitation is less than 500 mm and intense human activities play a dominant role in runoff.Fourth, in general catchments, runoff changes more significantly during relatively drier years or years with uneven precipitation dis-tribution. Finally, human-induced runoff change is related to both annual precipitation characteristics and operations of the reservoirs forcatchments in which reservoirs played a significant role. DOI: 10.1061/(ASCE)HE.1943-5584.0001078. © 2014 American Society ofCivil Engineers.

Author keywords: Human-induced runoff change; Precipitation distribution; Northeast China; Soil water assessment tool model.

Introduction

Climate change and human activities, which include land use/coverchange, hydraulic construction, and water withdrawal, significantlyimpact runoff and other hydrological processes. Separating theireffects is important to produce accurate predictions of future runoffand to provide useful information to regional water resource man-agement (Milly et al. 2005). Land use changes that gradually alterthe hydrological process have accelerated in pace during recentyears (Houghton 1994; Turner et al. 1994). Water conservancyproject constructions can dramatically change the downstream run-off routing of rivers (Williams and Wolman 1984), whereas artifi-cial water consumption, which directly decreases streamflow byremoving water from a stream, shows an increasing trend in mostChinese rivers, such as the Yellow River (Cong et al. 2009). Withrapid economic and demographic development, human interven-tions on runoff are becoming increasingly pronounced in China,against the background of global climate change.

Numerous studies have been conducted to study the impact ofhuman interventions and climate change on runoff. For instance,Ren et al. (2002) found that a sharp decline of runoff in theHaihe catchment was primarily driven by human activities, which

accounted for 79% of the total runoff change. Fu et al. (2004, 2007)concluded that variability in both human activities and climatechange was responsible for the water crisis in the Yellow River.Hao et al. (2008) indicated that the impacts of human activitieson the decrease of surface runoff in the mainstream of the TarimRiver were 42, 64, and 75% in the 1970s, 1980s, and 1990s, re-spectively. Fu et al. (2009) reported that the water withdrawals fromthree river basins in the North China Plain were 52, 82, and 88% ofinternal water resources. An investigation by Piao et al. (2010)showed that increased withdrawals can explain approximately 35%of the declining runoff observed at the Huayuankou station in thelower reaches of the Yellow River over the last half century. In theHuai River Basin, less than a quarter of the runoff was used inthe wet years, whereas more than half of the runoff was withdrawnin dry years because of local human activities at Bengbu station(Yang et al. 2010). A study in Dongjiang River Basin noted thathuman activities accounted for approximately 50% of the runoffdecrease during the low-flow period (Liu et al. 2010). A studyby Zhang et al. (2011) estimated that climate variability and humanactivities accounted for approximately 43 and 57%, respectively, ofthe reduction of the annual runoff accounted for in the Hun-TaiRiver basin in Northeast China.

Hydrological models are often used when studying the impactsof human activities. Catchment hydrological responses to land usechanges are simulated based on the actual land use from past yearsand plausible future land use scenarios (Thanapakpawin et al. 2007;Marshall and Randhir 2008). The effects of a given hydraulic struc-ture on the flow regime depend on its purpose, storage capacity, andoperating rules when considered in a simple water balance model(Güntner et al. 2004). Regarding artificial water consumption,water consumption data from industry, agriculture, and domesticsources can be entered into a model, such as a soil and water assess-ment tool (SWAT). The changes attributed to these factors can thenbe determined by simulating the scenarios and comparing the re-sults to the observed hydrologic characteristic value (Cong et al.2009). However, most studies have explored human-induced run-off change on a decadal scale; within that scale, they have rarely

1Postdoctoral Fellow, School of Hydraulic Engineering, Dalian Univ.of Technology, Dalian 116024, China. E-mail: aijing0313.zhang@gmail.com

2Professor, School of Hydraulic Engineering, Dalian Univ. of Technol-ogy, Dalian 116024, China (corresponding author). E-mail: czhang@dlut.edu.cn

3Postdoctoral Fellow, School of Hydraulic Engineering, DalianUniv. of Technology, Dalian 116024, China. E-mail: jgchu@mail.dlut.edu.cn

4Research Scientist, CSIRO Land and Water, Private Bag 5, Wembley,WA 6913, Australia. E-mail: Guobin.Fu@csiro.au

Note. This manuscript was submitted on November 7, 2012; approvedon July 25, 2014; published online on September 19, 2014. Discussion per-iod open until February 19, 2015; separate discussions must be submittedfor individual papers. This paper is part of the Journal of Hydrologic En-gineering, © ASCE, ISSN 1084-0699/04014069(13)/$25.00.

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analyzed its temporal differences or considered variations in annualprecipitation. Few existing studies that have considered the impactsof human interference on runoff in wet and dry years have revealedthat such impacts are larger in the drier years than in the wetteryears (Yang et al. 2010; Wang et al. 2010). Such conclusions havegained wide recognition because, as a rule, more water is neededfor domestic uses and to support farming in dry years. However, indifferent catchments, human activities vary by region and exerttheir impacts on water resources. For example, in the Haihe RiverBasin, catchments with a higher percentage of farmland areas hadstronger and earlier abrupt declines in runoff (Yang and Tian 2009).Hydrological process changes downstream of the dams, especiallyfor large reservoirs, are closely associated with the regulating ac-tivities of the reservoirs (Yang et al. 2008). Watersheds with highpopulation densities and high percentages of urban land are likelyto induce an increase in mean annual runoff owing to surface im-perviousness extension (Wang and Hejazi 2011). In Tianjin City ofChina, more water is taken from underground in dry years, produc-ing a nonsignificant effect on surface runoff (Sang et al. 2010).Consequently, it is necessary to thoroughly investigate the lawsof runoff change under the effects of different human activitiesand to determine whether human activities induce a larger runoffchange during dry years.

Complicated land shapes, diversified microclimates, and unbal-anced social and economic development initiate different humanimpacts on runoff among catchments in Northeast China. In thisstudy, seven typical catchments in Northeast China were selectedto analyze human-induced runoff change by using SWAT. Theoverall goal of this study is to determine how runoff changes inresponse to human activities and how these changes relate to pre-cipitation in Northeast China. More specifically, the objectives ofthe paper are: (1) to determine and map the degree of runoff changeattributable to human activities in different catchments, and (2) toexplore the characteristics of human-induced runoff change underdifferent annual precipitation conditions. This study will be helpfulfor water resource management in Northeast China under changingenvironmental conditions.

Materials and Methods

Study Area

Northeast China stretches between longitudes 115°05′ and 135°02′Nand latitudes 38°40′ and 53°34′E, covers an area of 1.24 × 106 km2

and has a population of approximately 120 million (Fig. 1). The cli-mate of Northeast China includes warm–temperate, temperate, andcool–temperate. Along longitudinal zones, it varies from humid, tosemihumid, to semiarid (Zhang et al. 2006). The annual precipita-tion, with irregular rainfall distribution, decreases from the southeast(900 mm) to the northwest (400 mm), and 70% of precipitation oc-curs during summer (Zhang et al. 2010). The mean annual air tem-perature varies spatially from −1 to 10°C.

Owing to its unique natural condition and resources, NortheastChina is not only one of the old industrial bases but also one of thebread baskets of China (Zhou et al. 2008). Approximately 15% ofthe nation’s total grain is produced in the region, and nearly 60% ofthe produced grain is exported to other regions of China as commod-ity, which can feed approximately 216 million urban residents an-nually (Cheng et al. 2010). Development in the region has beenbased on exploitation of water resources. Agricultural water demandin this region is so high that rainfall often cannot meet crop waterdemand for its quantity and annual distribution. Because irrigationsignificantly enhances harvests, it has become an indispensableagronomic practice. Together with industrial development and sharppopulation expansion, the exploitation and utilization of water re-sources have extended to a certain degree. The document MediumReservoir Planning of China (Ministry of Water Resources ofPeople’s Republic of China 2008) stated that more than 3,100 res-ervoirs with a total storage capacity of over 90 billionm3 were builtto supply water for agricultural, industrial, and domestic uses in theSonghua and Liao River basins until the end of 2006. Unreasonabledevelopment and use of water resources reduced runoff and initiatedmany serious water-related eco-environmental problems (Liu andLiu 2006). Close attention must be paid to understanding how runoffhas been changed by human activities in Northeast China.

Fig. 1. (Color) Locations of the study areas and hydro-meteorological stations

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Considering the integrity of the hydro-meteorological dataand the intensity of human activities, seven typical catchments(Fig. 1) were selected to study human-induced runoff change.Selected river catchments are Daling, with various human activ-ities; Huifa, with many small and middle sized water storagefacilities; Hunhe, with large reservoirs, large irrigation district,and large city; Hulan, with various human activities; Taizi, withlarge reservoirs; Tangwang, with few human activities; andWuyuer, with various human activities. Other relevant details aboutthe study areas are summarized in Table 1.

Data Set

The geospatial data used in this study include a terrain map, a soilmap, and a land use/cover map, as shown in Fig. 2. A digital eleva-tion model (DEM) with a spatial resolution of 90 m [ConsultativeGroup on International Agricultural Research-Consortium forSpatial Information (CGIAR-CSI) 2004] was available. A landuse map of the 1980s at a scale of 1∶100,000 was obtained fromthe Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences (CAS 2005). The original landuse data have six level-one classes, which are farmland, forest,

grassland, residential area, water body, and bare land. The area stat-istical data in these classes for the catchment above the selectedhydrologic station are given in Table 2. A soil map at a scale of1∶1,000,000 was also provided by the Data Center (CAS 2005).The soil database for the SWAT model has been reconstructed fol-lowing the approach described by Zhang et al. (2012a).

Daily meteorological data were collected from the ChinaMeteorological Data Sharing Service System (http://cdc.cma.gov.cn/), which include maximum and minimum temperature, solarradiation, humidity, and wind speed data. Daily precipitation andrunoff data were collected from the Hydrographic and WaterResources Bureau of Heilongjiang, Liaoning and Jilin province(Fig. 1 and Table 3). A handful of missing precipitation data werefilled in by using the Weather Generator (WXGEN) in the SWATmodel. The WXGEN uses Markov chain-skewed (Nicks 1974) orMarkov chain-exponential models (Williams 1995). A first-orderMarkov chain is used to define the day as wet or dry. When a wetday is generated, a skewed or exponential distribution is used togenerate the amount of precipitation. All area geospatial and hydro-meteorological statistical data referenced in the study are calculatedfor the catchments of the hydrologic station in each river basin.

Methodologies

Trend and Change Point Analysis

An examination of the historical variability of precipitation andrunoff can lead to a better understanding of the cause and degreeof runoff change. The Mann-Kendall (MK) trend analysis method(Mann 1945; Kendall 1975) was used in this study to analyzetrends in annual precipitation and runoff. The Mann-Whitney-Pettitt test method (Pettitt 1979) was used to identify the changepoint and its significance probability for runoff in the catchmentsunder investigation.

The MK test has been widely used for detecting hydro-meteorological trends around the world, including China. For atime series x1; x2; : : : ; xn, the standard normal statistic, Z, is esti-mated with the following formula:

Table 1. Information about the Study Catchments

RiverAreaa

(km2)

Averageannual

precipitation(mm)

Averageannualrunoff(mm)

Runoffcoefficient

Droughtindexb

Daling 9,913 486 65 0.13 2.6Huifa 12,412 712 194 0.27 1.2Hunhe 7,876 771 178 0.23 1.3Hulan 27,746 566 125 0.22 1.6Taizi 9,753 786 220 0.28 1.4Tangwang 19,110 604 247 0.41 1.2Wuyuer 8,372 516 69 0.13 1.8aDrainage area above the selected hydrologic station.bDrought index = average annual potential evaporation capacity/averageannual precipitation.

Fig. 2. (Color) DEM, soil, and land use (1960s) maps used in this study (data from CGIAR-CSI SRTMwebsite (http://srtm.csi.cgiar.org); Data Centerfor Resources and Environmental Sciences)

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Z ¼8<:

ðS − 1Þ= ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðSÞp

if S > 0

0 if S ¼ 0

ðSþ 1Þ= ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVarðSÞp

if S < 0

ð1Þ

where

S ¼Xn−1i¼1

Xnj¼iþ1

sgnðxj − xiÞ ð2Þ

sgnðθÞ ¼(1 if θ > 0

0 if θ ¼ 0

−1 if θ < 0

ð3Þ

VarðSÞ ¼ ½nðn − 1Þð2nþ 5Þ −Xt

tðt − 1Þð2tþ 5Þ�=18 ð4Þ

where t = extent of any given tie (xi ¼ xj) andP

t = summation of allties in Eq. (4); Z follows a standard normal distribution and a positive(negative) Z value denotes an upward (downward) trend. In a two-sided trend test, the null hypothesis that there is no significant trend isrejected at the significance level α if jZj > Zð1−α=2Þ, where Zð1−α=2Þis the critical value of the standard normal distribution with a prob-ability exceeding α=2. A trend-free prewhitening (TFPW) procedure(Yue et al. 2002; Ishak et al. 2013) was used to reduce the effects ofserial correlation before the MK test was applied.

Change point analysis of runoff can detect the start of the periodwith significant effects of human activities. In the Mann-Whitney-Pettitt test, the time series x1; x2; : : : ; xn was considered as twosamples, x1; : : : ; xt and xtþ1; : : : ; xn. Next, the Pettitt indicescan be calculated as follows:

Ut;n ¼Xt

j¼1

Xni¼1

sgnðxj − xiÞ ðt ¼ 1; : : : ; nÞ ð5Þ

where

sgnðθÞ ¼

8><>:

1 if θ > 0

0 if θ ¼ 0

−1 if θ < 0

ð6Þ

where t = most probable change point when jUt;nj reaches peak.The probabilities for change point can be calculated by the follow-ing formula:

pðtÞ ≅ 2 exp½ð−6U2t;nÞ=ðn3 þ n2Þ� ð7Þ

A two-sample t-test method (Crawshaw and Chambers 1990;Utts and Heckard 2002) was also used in this study to examinethe difference between the samples before and after the testedchange point.

Precipitation Clustering

Previous studies on the impact of human activities on runoff seldomconsidered annual precipitation and its distribution. Allowing foruneven distribution characteristics of the annual and interannualprecipitation in a region and the sample size of annual precipitation,two clustering eigenvalues were selected in this study: annualamount of precipitation (in a calendar year) and nonuniform coef-ficient of precipitation (Zhang and Qian 2003).

The nonuniform coefficient of precipitation, Cv, is a parameterthat properly represents the annual distribution of precipitation.When the nonuniform coefficient is greater, the disparity is widerbetween monthly precipitations. Cv can be expressed as

Cv ¼ σ=R̄ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

12

X12i¼1

ðRi − R̄Þ2vuut =

1

12

X12i¼1

Ri ð8Þ

where Riði ¼ 1; 2; : : : ; 12Þ = monthly precipitation and R̄ = meanvalue of the monthly precipitation.

After preparing the annual amount of precipitation and the seriesof precipitation nonuniform coefficients, precipitation is classifiedinto four types based on the following steps. In each catchment, theprecipitation of all years is first classified into two classes accordingto the frequency computation of the annual amount of precipitation(p1 < 50% and p1 > 50%). Next, the precipitation of each class isgrouped into two groups based on the frequency computation of theprecipitation nonuniform coefficients (p2 < 50% and p2 > 50%).Finally, the precipitation of all years can be divided into four types,i.e., A (p1 < 50% and p2 < 50%), B (p1 < 50% and p2 > 50%),C (p1 > 50% and p2 < 50%), and D (p1 > 50% and p2 > 50%).

Hydrological Model

Basin-based continuous rainfall-runoff models for the seven studycatchments were constructed by using ArcSWAT 2.1 to evaluatehuman-induced runoff change. SWAT is a physically based, longterm, and semidistributed hydrological model. It was developed topredict the impact of land management practices on the hydrologicand water quality response of complex watersheds with hetero-geneous soils and land use conditions (Arnold et al. 1998). Awater-shed is partitioned into subwatersheds, and input information foreach subwatershed is organized into the following categories inthe model: climate, hydrologic response units (HRUs), ponds/wetlands, groundwater, and the primary reach runoff concentration.

Table 3. Information about the Hydro-Meteorological Data Used in ThisStudy

River

Daily data for the hydro-meteorological stations

Hydrometric Meteorological Precipitation

Name Duration Number Duration Number Duration

Daling CY 1956–2006 5 1956–2006 20 1956–2006Huifa WD 1955–2005 5 1956–2006 14 1956–2005Hunhe SY 1957–2006 3 1956–2006 19 1956–2006Hulan LX 1953–2005 6 1956–2006 44 1962–2001Taizi XL 1953–2006 3 1956–2006 22 1956–2006Tangwang CM 1963–2005 5 1956–2006 24 1963–2001Wuyuer YA 1957–2001 4 1956–2006 10 1962–2001

Note: CM = Chenming; CY = Chaoyang; LX = Lanxi; SY = Shenyang;WD = Wudaogou; XL = Xiaolinzi; and YA = Yiandaqiao.

Table 2. Land Use Conditions for the Selected Catchments in the Early1980s

River

Proportion of the land use type (%)

Forest Grassland

Farmland

WaterResidential

area OthersPaddyfields

Dryland

Daling 21.7 29.5 0.1 43.7 1.5 3.5 0.0Huifa 50.9 2.9 15.1 26.1 1.4 3.5 0.1Hunhe 70 1.1 3.6 19 2.2 3.9 0.2Hulan 29.1 2.3 9.2 45.8 1.8 3.1 8.7Taizi 56.7 1.2 7.3 25.8 2.4 6.4 0.1Tangwang 84.9 8.4 0.0 4.7 0.2 0.8 1.0Wuyuer 12.8 3.5 0.4 65.9 0.7 3.9 12.8

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Flows generated from each HRU are summarized and routedthrough channels in a subwatershed.

The model was selected because of its availability and ease inhandling input data. In recent years, it has been successfully usedfor many river basins in China (Sang et al. 2010; Fan et al. 2010;Nie et al. 2011; Zhang et al. 2012a, b).

Calculation of Human-Induced Runoff Change

Many studies address the effects of climate change and human ac-tivities on runoff processes. Two methods are primarily used, onefor long-term analysis of records and the other for hydrologicalsimulation. Long-term hydrological data show the temporal varia-tions in runoff to be influenced by climate and land cover changes.Analyses of such changes from long-term hydrological data cannotonly identify runoff changes but also reveal the influences of cli-mate change and human activities in a catchment (Zhang et al.2011). However, this method ignores nonlinear processes betweenprecipitation and runoff and the conditions of land use/cover.Hydrological simulation is a powerful method to analyze runoffresponses to climate change and different scenarios, although itpossesses some uncertainties. It has been widely used for study-ing runoff change attributable to various types of driving factors(Seguis et al. 2004; Ren et al. 2007; Fan et al. 2010; Nie et al.2011). The hydrological simulation method was chosen in thisstudy because it can clearly simulate the mechanism of runoffformation.

The following steps are necessary when a hydrological simula-tion method is used to evaluate human-induced runoff change:(1) divide the study period into a predeveloped period with nonsig-nificant human impacts and a postdeveloped period, which isassociated with significant human impacts; (2) calibrate the param-eters of the hydrological model during the predeveloped period torepresent hydrological parameters under natural runoff conditions;(3) drive the calibrated hydrological model during the postdevel-oped period by changing only the meteorological input to recon-struct natural runoff; and (4) determine human-induced runoffchanges by comparing the reconstructed natural runoff with theobserved runoff in the same period. The difference between the ob-served and reconstructed runoff is meant to convey human-inducedrunoff change, which can be computed from Eqs. (9) and (10)

Rh ¼ Robs − Rn ð9Þ

H ¼ ðRh=RnÞ × 100% ð10Þwhere Rh = human-induced runoff change; Robs = observed runoffinfluenced by both climate change and human activities; Rn =reconstructed natural runoff influenced only by climate change;and H = percentage of human-induced runoff change.

Results and Discussions

Trend and Change Point Analysis of Precipitation andRunoff

Trends of Precipitation and RunoffVariations of the observed precipitation and runoff in the studiedcatchments are shown in Fig. 3. MK test Z-values of the annualprecipitation, precipitation nonuniform coefficients, and runoffseries were calculated for seven catchments.

For annual precipitation, two catchments, Daling and Huifa,show a slight upward trend during the study period with MK test

Z-values of 0.33 and 0.16, respectively. Trends of the precipitationin the other five catchments are slightly downward during the studyperiod. MK test Z-values of Hulan, Hunhe, Taizi, Tangwang, andWuyuer are −0.71, −0.46, −1.15, −0.12, and −1.09, respectively.None of the upward or downward trends are statistically significantat the level of α ¼ 0.05. There are also no prominent changes in thetime series of precipitation nonuniform coefficients at the signifi-cant level of α ¼ 0.05. MK test Z-values of Daling, Hulan, Hunhe,Huifa, Taizi, and Wuyuer are −1.56, 1.29,−0.68, 059,−0.96, 0.14,and −0.21, respectively.

Annual runoff sequences for the five catchments show a statisti-cally significant downward trend at the level of α ¼ 0.05, withZ-values as follows: Daling, −3.61; Hunhe, −3.97; Hulan, −2.38;Taizi, −2.16; and Wuyuer, −1.96. For the Huifa catchment, theZ-value is −1.86, with a downward trend that gains statistical sig-nificance at the level of α ¼ 0.1. Runoff for the Tangwang Rivercatchment shows a nonsignificant downward trend with Z-valueof −0.42.

Trend analyses of annual precipitation series indicate no ob-vious change in annual precipitation; trend analyses of the nonuni-form coefficient series defined by monthly precipitation suggestno obvious change in monthly precipitation. However, there is asignificant downward trend for runoff in all selected catchmentsexcept Tangwang. Changes in precipitation should not be the dom-inant factors that caused runoff reduction.

Change Points of Precipitation and Runoff

Results of change point analysis for precipitation and annual runoffat the selected hydrological stations are listed in Table 4.

Change point analysis results for precipitation show that at thesignificance level of α ¼ 0.05, there is no significant change pointfor precipitation at all study catchments. At the same significancelevel, the two-sample t-test method also shows that there is no sig-nificant change for the two precipitation samples before and afterthe change point in each catchment.

However, the scenarios are very different for the runoff series.At the significance level of α ¼ 0.05, there is no significantchange point for runoff of the Huifa and Tangwang Rivers.The two-sample t-test method was also used to test the differencebetween the two independent samples before and after the runoffchange point in each catchment. The null hypothesis, that twosamples separated by the change points have the same mean level,was rejected at the significance level of α ¼ 0.05 at all catch-ments. This means that runoff decreased significantly after thechange point in all studied catchments except Tangwang River.

Division of the Predeveloped and PostdevelopedPeriods

For Taizi and Tangwang Rivers, the change point of precipitationand runoff occurred at the same time. The precipitation changepoint of the two catchments is not significant. This implies thatthe runoff change is not induced by precipitation change in thesecatchments. Fig. 4 shows the regression lines of runoff and precipi-tation both before and after the change point (the Tangwang catch-ment is not included). The figure suggests that, for similar annualprecipitation, runoff before the change point is greater than thatduring the period afterward. Thus, runoff reduction is driven byother factors.

According to the results of the runoff change point test, in allcatchments except Tangwang River, the period of 1964–1979hosted an abrupt decline. Huifa and Wuyuer Rivers are the catch-ments with the earliest abrupt change, in which runoff changes

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started as early as 1964. In the Daling, Hulan, Hunhe, and TaiziRivers, abrupt runoff change occurred in the 1970s, approximately10 years later than the Huifa and Wuyuer Rivers. Thus, consider-able human events in and around the 1960s and 1970s are consid-ered the most plausible drivers for runoff decline in the catchments.

Robust population growth is a key feature of human activitiesduring 1960s and 1970s. In 1958, during the “great leap forward,”tens of thousands of demobilized soldiers rushed toward NortheastChina, founding scores of large state farms (Yi 2000). From 1966 to1976, Northeast China accepted millions of young intellectuals

Fig. 3. (Color) Variation of the observed precipitation, runoff, and reconstructed runoff

Table 4. Change Points of Precipitation and Runoff for the Study Catchments

River

Change point Probability Predevelopedperiod

PostdevelopedperiodPrecipitation Runoff Precipitation Runoff

DL 1961 1979 0.653 0.001 1956–1979 1980–2006HF 1996 1964 0.483 0.165 1955–1964 1965–2005HH 1975 1975 0.343 0.002 1956–1975 1976–2006HL 1963 1973 0.469 0.021 1953–1973 1974–2005TZ 1975 1975 0.312 0.012 1953–1975 1976–2006TW 1997 1997 0.418 0.37 — —WY 1998 1964 0.438 0.046 1957–1964 1964–2001

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aimed at land reclamation (Li et al. 2009). After 1974, collectiveland reclamation was brought into a state plan and given appropri-ate subsidy, consequently speeding up land development and forestand grass devastation (Compiling Committee of China’s NaturalResources 1995). To cope with irrigation problems, many pondsand reservoirs were constructed during this period. Statistical dataindicate that approximately 75% of large reservoirs, 66% ofmedium-sized reservoirs, and 90% of small reservoirs in Chinawere built between 1957 and 1977. A similar studies also indicatethat human activities are the primary driving factors affecting run-off change (Zhang et al. 2011, 2012a). Therefore, it can be arguedthat the primary driving factors inducing the sudden reduction inrunoff in Northeast China were sharply increased population, un-reasonably rapid land development, high water utilization for grainproduction, and construction of massive water conservancy projectsrelative to agricultural and domestic water use.

Overall, the period before the change point was defined as thepredeveloped period and the period thereafter was defined asthe postdeveloped period (Table 4).

Calibration and Validation of the SWAT Model

After parameter sensitivity analysis with the SWAT model, thefirst eight sensitive parameters of each catchment were selectedfor manual calibration. Whenever possible, the calibration and val-idation periods were kept within the predeveloped period. Leavingthe first two years as the warm-up period, observed data for thenext four years were used for model calibration, and the next fouryears were used for model validation. Because various intensehuman activities in the 1980s can significantly influence basinhydrologic processes, their impacts must be included for calibra-tion of a hydrological simulation model; however, the model could

not be accurately calibrated and verified with the data in the1980s. Therefore, the model was calibrated and verified with datafrom the 1960s because the change point analyses indicated thatsignificant changes occurred in the 1970s. Because the land usedata for the 1960s were not available, and the primary humanactivities occurred between the 1960s and 1980s in the study re-gions, the land use data for the 1960s were generated by adjustingthe land use map for the 1980s. Observed monthly operation dataof large reservoirs constructed in Hunhe and Taizi catchmentswere included in the SWAT model during calibration. The simu-lated results without reservoirs were assumed to represent naturalrunoff, as discussed later.

For the calibration and validation period, the Nash–Sutcliffe co-efficient of efficiency value, Ens (Nash and Sutcliffe 1970; Guptaet al. 1999), correlation coefficient, R, and relative error, Re (Nieet al. 2011), of the observed and simulated runoff were selected toevaluate the performance of the SWAT model. Table 5 lists theevaluation results, and Fig. 5 shows simulated and observed runofffor the calibration and validation periods on a monthly basis. Ex-cept for the Wuyuer river catchment, the evaluation results of Ens

are all greater than 0.7, R values are all greater than 0.85, and Re

values are all less than 8.7% for calibration and validation. Simu-lation results for the Wuyuer catchment are not as accurate as forthe other six catchments. The change point for the runoff in theWuyuer catchment is 1964; thereafter, the runoff should have beensignificantly affected by human activities. However, the SWATmodel was calibrated from 1964 because of a lack of precipitationdata. This may be the cause of the imperfect simulation results forthe Wuyuer catchment. On the whole, SWAT performance for themonthly runoff simulation is acceptable, according to the perfor-mance criteria given by Moriasi et al. (2007).

Fig. 4. Relationship between precipitation and runoff for predevelopment and postdevelopment periods

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Table 5. Evaluation Results of the Simulated Runoff

River

Monthly Yearly

Calibration period Validation period Calibration period Validation period

Ens R Re (%) Ens R Re (%) Re (%) Re (%)

DL 0.92 0.96 4.6 0.76 0.92 6.9 3.7 3.9HF 0.86 0.94 −8.7 0.86 0.93 −3.1 2.3 3.3HH 0.92 0.97 −2.1 0.95 0.98 0.1 −4 2.3HL 0.85 0.92 2.2 0.73 0.86 −4.1 2.7 −4.6TZ 0.88 0.98 −7.8 0.89 0.95 5.6 −2.3 5.9TW 0.80 0.91 0.0 0.86 0.93 0.4 0.8 −0.6WY 0.68 0.84 7.2 0.76 0.90 −11.8 −3.3 −5.5

Fig. 5. Simulated and observed runoff for the calibration and validation periods

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Natural Runoff Reconstruction

After calibration and validation of the SWAT model, natural runoffwas reconstructed by changing only the meteorological input, with-out changing any calibrated parameters or local human activities.The annual natural runoff for each catchment was constructed toisolate the human-induced runoff change, as shown in Fig. 3.

Comparison of the reconstructed and observed runoff valuesfrom the Tangwang River catchment shows great consistency. Thisdemonstrates that reconstructed and observed runoff are basicallythe same in areas without serious human activities. However, in theother six catchments, as time passed, the gap between observed andreconstructed runoff increased. This phenomenon suggests increas-ing levels of human-induced runoff changes during the last fivedecades. It is also found that the large reservoirs in Hunhe andTaizi catchments extensively changed runoff by storing water inwet years and releasing water in drought years. This sole elementcaused observed runoff to be smaller in wet years and larger indrought years than natural runoff. In the Taizi catchment, the func-tion of multiyear regulation of the large reservoirs appears lessobvious than in the Hunhe catchment.

Analysis of Human-Induced Runoff Change

The runoff change attributable to human activities was determinedby comparing reconstructed with observed runoff in the sevencatchments. Although there was no great change for the runoffin Tangwang, runoff changes attributable to human activities werecalculated for comparison. Scientific differences between observedand natural runoff became obvious at approximately 1970 in mostcatchments (Fig. 3); human-induced runoff changes were calcu-lated from 1970 in the following. Human-induced runoff changesfrom 1970 were calculated yearly and the data are listed in Fig. 6.Reduction in decadal runoff attributable to human activities from1970 to 2001 was calculated for each catchment, as shown inFig. 7. The results of human-induced runoff change under differentdegrees of precipitation were also calculated and are shownin Fig. 8.

Effect of Human Activities on Annual Runoff inDifferent Catchments

Fig. 6 shows that for the Tangwang River catchment, which wasinsignificantly affected by human activities, runoff change fluctu-ates around zero. However, the human-induced runoff changes ofthe other six catchments confirm an increasing trend of human dis-turbance, although the values differ from catchment to catchment.

The range of human-induced runoff change in the Hunhe catch-ment is greater than that of the other catchments, which may bea function of the multiyear adjustable reservoir.

The percentage of human-induced runoff decrease is greatestin the Daling catchment, which has the lowest average annual

Fig. 6. (Color) Yearly human-induced runoff changes

Fig. 7. (a) Percentage of runoff changes attributable to human activitiesin different periods; (b) quantity of runoff changes attributable tohuman activities in different periods

Fig. 8. (a) Percentage of human-induced runoff changes under differ-ent conditions of precipitation; (b) quantity of human-induced runoffchanges under different conditions of precipitation

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precipitation and the largest drought index. As expected, the humaninduced runoff decrease is lowest in the Tangwang catchment,which is not exposed to significant human activities. The orderof the catchments with respect to the degree of human influenceon runoff reduction [Fig. 7(a)], from largest to smallest, is Daling(−42.7% per year), Wuyuer (−30.2% per year), Huifa (−22.4%per year), Hulan (−18.9% per year), Hunhe (−15.6% per year),Taizi (−13.8% per year), and Tangwang (−0.1% per year).

Owing to different amounts of precipitation, the quantities ofwater resources are different in each catchment. This differenceleads to a lack of agreement between the amount of human-inducedrunoff changes and the proportion of runoff reduction (percentageof human-induced runoff change). The quantities of human-induced runoff change in Daling, Wuyuer, Huifa, Hulan, Hunhe,Taizi, and Tangwang catchments are −42.08, −22.10, −53.03,−34.34, −32.39, −26.02, and −4.6 mm=year, respectively[Fig. 7(b)].

Effect of Human Activities on Annual Runoff inDifferent Periods

The percentage of runoff reduction that is attributable to humanactivities [Fig. 7(a)] indicates that human interference with runoffhas become increasingly serious. The decrease was greater than30% in the Daling catchment and nearly 20% in the Wuyuerand Huifa catchments as early as the 1970s, when urbanizationand industry were developing. Thus, the runoff decrease was rea-soned to be caused by the earlier development of agriculture. Run-off at the Hunhe and Taizi catchments was smaller in the 1970s,increased to no more than 10% per year in the 1980s, and thenincreased to approximately 20% per year in the 1990s. During2000–2006, the percentage of runoff reduction in the Taizi Riverincreased slightly, whereas that in the Hunhe catchment increasedsharply to more than 60% per year. These changes may be not onlybecause the percentage of farmland in the Hunhe catchment isgreater than that in the Taizi catchment but also because of thedramatic increase in industry and in urban residential water use.Shenyang, the fifth largest city of China, is largely located in themiddle and lower reaches of the Hunhe River basin, with a pop-ulation of approximately 5 million (Yu et al. 2007). Water resourcesin the Hunhe catchment are the primary source of industrial, do-mestic, and ecological water for Shenyang. Statistics (Bureau ofWater Resources of Shenyang Municipality 2007) show that from2001 to 2007, the total water use of Shenyang city has increasedfrom 22.7 × 108 to 29.2 × 108 m3.

The increased tendency toward human-induced runoff change[Fig. 7(b)] is basically the same as the percentage of runoff reduc-tion [Fig. 7(a)], except in the Daling catchment. In Daling, the re-duction in human-induced runoff in the 1980s was smaller than thatin the 1970s, and that in the 2000s was smaller than that in the1990s. The Daling catchment, with an average annual precipitationof less than 500 mm per year and panevaporation of more than1,700 mm, is noted for its severe shortage of water resourcesand uneven distribution. Paradoxically, the demand on water re-sources for industry, agricultural production, and daily activitiescontinuously increases. The total amount of water use increasedfrom 2.01 × 108 m3 in 1980 to 3.76 × 108 m3 in 2006 for thecatchment of Chaoyang station (Zhang and Zhou 2011). The pre-cipitation was 512 mm in the 1970s, 430 mm in the 1980s, 529 mmin the 1990s, and 461 mm in the 2000s. Runoff levels in the 1980sand 2000s are relatively small, corresponding to less precipitation.A lack of usable water resources may be the greatest potential rea-son that human-induced runoff reduction was smaller during the1980s and 2000s.

Human-Induced Runoff Changes under DifferentPrecipitation Conditions

Precipitation plays a determining role in the course of human im-pacts on the utilization and management of water resources. There-fore, the characteristics of human-induced runoff change underdifferent annual precipitation conditions are discussed.

On the basis of precipitation clustering methods, the precipita-tion of all years on record was classified into four types (A, B, C,and D) in each catchment. The numbers for all types of precipita-tion are shown in Fig. 9. On the whole, human impact was asso-ciated with runoff reductions at all catchments under all types ofprecipitation conditions, except under Form D precipitation inthe Hunhe catchment (Fig. 8). Human-induced runoff changesunder different precipitation conditions for each catchment are dis-cussed in more detail in the following.

For the Daling catchment, the percentage of human-inducedrunoff reduction was nearly at the same level (approximately45–50%) under all types of precipitation conditions [Fig. 8(a)].Fig. 8(b) shows that human-induced runoff changes reach a maxi-mum value for precipitation of Form A (annual precipitation iscomparatively concentrated and high) and a minimum value ofForm D (annual precipitation is comparatively even and low).There are many reasons for this situation, but the chief causesmay be intense human activities and scant precipitation. The Dalingcatchment is located in an arid and semiarid region with an averageannual precipitation of only 480 mm. Farmland accounts for 25%of the total area, which demands high levels of irrigation water. Inaddition, the demand for water resources increased sharply in thecity and county along with rapid socioeconomic development.Water withdrawal for industrial, agricultural, and domestic useswas up to 3.35 × 108 m3 at Chaoyang Station in 2005, accountingfor approximately 38% of the total runoff of approximately 8.8×108 m3. Water resources in this location are far from sufficient: thegreater the amount of runoff, the greater the amount of water use.Therefore, the percentage of human-induced runoff reduction haslittle association with the distribution of annual precipitation; itmerely rises as precipitation rises.

The percentage of human-induced runoff reduction for Hulan,Taizi, and Wuyuer catchments [Fig. 8(a)] is greater under the pre-cipitation of Forms C and D (with less annual precipitation) thanForms A and B (with more annual precipitation). The percentage ofhuman-induced runoff reduction for A is slightly greater than for B,and human-induced runoff for C is slightly greater than for D.These representations suggest the conclusion that the proportionof human influence on runoff is greater when annual precipitationis less in amount and more uneven in distribution in the Hunhe,Hulan, and Wuyuer catchments.

Fig. 9. Amounts of all types of precipitation

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In the Hunhe catchment, the percentage of human-induced run-off change reduced the most for Form C precipitation. Distinct fromthe other catchments, human activity induced runoff increases forForm D precipitation. This is likely attributable to the operation ofthe large reservoir Dahuofang. The Dahuofang Reservoir, with acatchment area of approximately 46.6% of the Hunhe River water-shed and a total capacity of 2.181 billionm3, is located at the upperreaches of Hunhe River. It is currently an important water resourcefor the cities of Shenyang and Fushun and is the primary waterresource of the Hunpu Irrigation District (Dong 2008). The ob-served runoff for Shenyang Station (approximately 60 km down-stream of the Dahuofang reservoir) used in this study is sensitive tothe operation of the reservoir. As a multiyear adjustable reservoir, ituses water stored in wet years to guarantee a water supply in dryyears. It is this function that induced an increase in runoff forForm D precipitation.

In the Huifa catchment, the percentage of human-induced runoffreduction under Form B precipitation is greater than that of FormA. The existence of many midsized and small reservoirs is the pri-mary factor influencing runoff in the Huifa catchment (Zhang et al.2012b). There is one large reservoir, 12 midsized reservoirs, 476small reservoirs, and countless ponds in the Huifa catchment ofWudaogou Station, with a total capacity of 6.42 × 108 m3 (Caoet al. 2011). Precipitation in the Huifa catchment is primarily con-centrated in the summer and autumn, and July to August amount to44.7% of the total. The more concentrated the precipitation distri-bution, the greater its proportion that turns into runoff. Because inyears with concentrated precipitation, midsized reservoirs cannotbe operated with full storage to prevent floods and maintain thesafety of reservoir, small-sized reservoirs and ponds would fill fullyover a short time. When this occurs, runoff has a lower probabilityto turn into evaporation owing to increased water surface and hy-dropower generation and decreased opportunity to supply agricul-ture and municipal water consumption. These are the most likelyreasons for the greater human-induced runoff reduction under FormB precipitation than A.

Following common sense, it may be expected that a great dealof water is used for agricultural irrigation during dry years, andtherefore less water is left in rivers. However, for the catchmentsunder investigation, not all of the average human-induced runoffreduction reached maximum under Forms C or D precipitation[Fig. 8(b)]. In addition, human-induced runoff changes (Rh) werecalculated under different grades of precipitation (Table 6). Thesecalculations also show that average Rh does not reach maximumwhen precipitation reaches its minimum for all catchments. Thesesituations may be because there was insufficient water for humanconsumption in extremely dry years in a certain catchment, sowater was obtained from another source, e.g., underground water(Sang et al. 2010) or interbasin water transfer.

Discussion on Uncertainty

There are some uncertainties in the calculated human-induced run-off change. First, only short-term hydro-meteorological data duringthe predeveloped period were used for the SWAT model calibrationand validation. Human disturbances may have existed to some ex-tent in the predeveloped period. Second, the spatial resolution ofthe DEM, land use/cover, and soil data and the spatial densitiesof the hydroclimatic data influence the performance of the simu-lation results from the SWAT model. Finally, although most param-eters in the hydrological model are estimated based on field or landuse data in SWAT, they also influence the performance of the sim-ulation results (Zhang et al. 2009). However, the performance of theSWAT in simulating annual runoff in the study indicated that theT

able

6.Hum

an-Induced

RunoffChanges

underDifferent

Amountsof

AnnualPrecipitatio

n

Daling

Huifa

Hulan

Hunhe

Taizi

Wuyuer

P (mm)

Rh

(mm=y

ear)

H (%)

P(m

m)

Rh

(mm=y

ear)

H (%)

P(m

m)

Rh

(mm=y

ear)

H (%)

P(m

m)

Rh

(mm=y

ear)

H (%)

P(m

m)

Rh

(mm=y

ear)

H (%)

P(m

m)

Rh

(mm=y

ear)

H (%)

<400

−2.2

−9.1

<600

−46.8

−38.9

——

—<600

25.7

30.3

<600

−25.9

−18.6

<400

−15.3

−50.2

400–

500

−40.2

−53.4

600–

700

−50.9

−29.5

<500

−11.5

−16.7

600–700

−23.5

−16.6

600–700

−49.4

−22.3

400–500

−20.9

−41.9

500–

600

−54.1

−43.4

700–

800

−56.7

−22.5

500–

600

−35.2

−30.7

700–800

−94.3

−45.4

700–800

−36.3

−12.6

500–600

−23.2

−26.3

>600

−91.1

−45.2

>800

−60.8

−17.1

>600

−27.2

−13.6

>800

−71

−20.1

>800

−34.9

−7.5

>600

−48.4

−31.1

Note:

H=percentage

ofhuman-induced

runoffchange;P

=annual

precipitatio

n.

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calibration and validation of the SWAT model, showing the rel-ative error of annual runoff at each of the analyzed stations (Table 5)was within �6%; this is far less than the magnitude of runoffchange impacted by climate variability and human activities. Addi-tionally, satisfactory agreement for simulated and observed runoffat the Tangwang River, which was slightly affected by human ac-tivities, made the results of the human-induced runoff change morecredible.

The limited sample may lead to a degree of uncertainty inthe analysis results. Although human-induced runoff change hasobvious regularity in different catchments under different precipi-tation conditions, some samples (Fig. 9) are less than 10 years,which may affect the quantification of the results. The regularitiesof human-induced runoff change in the selected catchments pre-sented in this study may not represent other river basins withrespect to the regional features of human activities. Future researchis planned to examine some of these sources of uncertainty.

Conclusions

Human-induced runoff changes for seven catchments in NortheastChina were evaluated by the SWAT model in this study. With theaim to analyze the features of human-induced runoff change indifferent catchments, annual runoff changes attributable to humanactivities under different times and patterns of precipitation weredetected. The primary achievements of this study were to exposethe features of human-induced runoff changes in different catch-ments and reveal their distinctions under different annual precipi-tation patterns.

Although changes in precipitation were insignificant in North-east China over the last five decades, results of the MK test indicatethat runoff decreased significantly in six catchments that areheavily influenced by human activities but nonsignificantly inthe forest catchment of Tangwang. This implies that runoff declinein most catchments was not driven by precipitation changes or var-iations. Points of abrupt change for runoff (generally in the 1970s)were detected for all catchments; these change points were consis-tent with the occurrences of human events during those periods.Further analysis reveals that under similar precipitation, there is lessrunoff in the postdevelopment period. This confirms that humanactivities, rather than changes in precipitation or climate, are theprimary driving factors in runoff decline in Northeast China.

Natural runoff was reconstructed in each catchment with theSWAT model. Comparisons of the observed and reconstructednatural runoff show that impacts of human activities on annual run-off in the study catchment deepened gradually with time. The im-pacts of human interference on annual runoff changes are greater inarid regions and dense farming catchments. Runoff changes incatchments with large reservoirs were mostly determined by theoperating forms of the reservoirs.

Human activities directly change the process of annual runoff,and annual runoff processes are primarily dominated by precipita-tion, which determines the means by which human activitieschange runoff. Based on this view, features of human-induced run-off changes under different patterns of annual precipitation wereanalyzed. Human-induced runoff reduction has little associationwith the annual distribution of precipitation in arid areas. For gen-eral catchments, the smaller the amount of precipitation, the greaterthe human influence on runoff change; the more uneven the distri-bution of precipitation, the greater the human influence on runoffchange. For catchments in which reservoir function is very effec-tive, human influences on runoff changes are closely related to thefeature and operating forms of the reservoirs.

All of these findings will enrich current knowledge aboutthe impacts of human activities on runoff and provide quantitativeinformation for water resource planning and management inNortheast China.

Acknowledgments

This work is supported by the National Natural Science Foundationof China (Grant No. 51320105010 and 51279021). Dr. GuobinFu is supported by the Australia China Research Centre on RiverBasin Management. The two anonymous reviewers and editors areappreciated for their constructive comments on the manuscript.

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